Image segmentation: Road hazard detection using u-net and attention mechanism

Ensuring the safety and efficiency of intelligent transportation systems relies heavily on the accurate segmentation of various elements present on roadways. Conventional image segmentation techniques often fall short when tasked with identifying a wide variety of road hazards—such as vehicles, pedestrians, lane markings, traffic signs, potholes, and speed breakers—particularly under difficult conditions like poor lighting or partial obstruction. This research presents an enhanced image segmentation model that leverages the strengths of the U-Net architecture, augmented with a spatial attention mechanism, to deliver precise and dependable detection of essential road features. The fusion of U- Net’s multi-scale feature learning capabilities with attention-based refinement allows the model to better interpret complex visual scenes and maintain high accuracy across diverse scenarios. Evaluations conducted on varied datasets confirm the effectiveness of the proposed framework in detecting a broad spectrum of road components, highlighting its potential for real-time deployment in autonomous navigation and traffic monitoring systems.